alexa Estimation of Missing River Flow Data for Hydrologic Analysis: The Case of Great Ruaha River Catchment | OMICS International| Abstract
ISSN: 2157-7587

Hydrology: Current Research
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  • Research Article   
  • Hydrol Current Res 2018, Vol 9(2): 299
  • DOI: 10.4172/2157-7587.1000299

Estimation of Missing River Flow Data for Hydrologic Analysis: The Case of Great Ruaha River Catchment

Lusajo H Mfwango*, Catherine J Salim and Shija Kazumba
Department of Irrigation, Water Institute, , Dar es Salaam, Tanzania
*Corresponding Author : Lusajo H Mfwango, Department of Irrigation, Water Institute, P.O.Box 35059, Dar Es Salaam, Tanzania, Tel: +63280009456, Email: [email protected]

Received Date: Mar 24, 2018 / Accepted Date: Apr 05, 2018 / Published Date: Apr 11, 2018


Availability of data on hydrologic variables such as river flow is necessary for planning and management of water resources. Many developing countries Many River basins in developing countries has no complete dataset on river flow due to degradation of gauging stations gauging stations coupled with unsatisfactory data compilation unsatisfactory data compilation and storage procedures. Different methods are available to fill missing data; however, these methods differ in performance depending on the characteristics of initial data points. The purpose of this study was to fill the missing data in the Great Ruaha River by selection of best method. In this study, simple and multiple regression analysis, and recession methods have been employed to fill the gaps of missing river flow data on ten gauging stations of Great Ruaha River catchment. Performances of these methods were assessed using Nash-Sutcliffe efficiency, Root Mean Square Error and Mean Absolute Error. The results showed that, Multiple regressions are suitable over Linear regression method for missing data during the period of high flow, however selection of either method depends on the availability of data availability on independent variable. Recession method was found to be suitable for filling missing data during the period of low flow. Though these methods were useful in filling data, the challenge was that more than one method was required to estimate all the missing data at a gauging station. This is because, missing data at a given gauging station were experienced during dry and rain seasons.

Keywords: Great Ruaha river; Missing river flow data; Regression analysis; Recession method; Nash-Sutcliffe model; Root mean square error; Mean absolute error (MAE)

Citation: Mfwango LH, Salim CJ, Kazumba S (2018) Estimation of Missing River Flow Data for Hydrologic Analysis: The Case of Great Ruaha River Catchment. Hydrol Current Res 9:299. Doi: 10.4172/2157-7587.1000299

Copyright: © 2018 Mfwango LH, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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